Applied Intelligence 23, 267–275, 2005
c 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.


Pitch-Dependent Identification of Musical Instrument Sounds∗
TETSURO KITAHARA
Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Sakyo-ku,
Kyoto 606-8501, Japankitahara@kuis.kyoto-u.ac.jp

MASATAKA GOTO
“Information and Human Activity”, PRESTO, JST/National Institute of Advanced Industrial
Science and Technology, Tsukuba, Japanm.goto@aist.go.jp

HIROSHI G. OKUNO
Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Sakyo-ku,
Kyoto 606-8501, Japanokuno@i.kyoto-u.ac.jp

Abstract. This paper describes a musical instrument identification method that takes into consideration the pitch
dependency of timbres of musical instruments. The difficulty in musical instrument identification resides in the pitch
dependency of musical instrument sounds, that is, acoustic features of most musical instruments vary according
to the pitch (fundamental frequency, F0). To cope with this difficulty, we propose an F0-dependent multivariate
normal distribution, where each element of the mean vector is represented by a function of F0. Our method firstextracts 129 features (e.g., the spectral centroid, the gradient of the straight line approximating the power envelope)from a musical instrument sound and then reduces the dimensionality of the feature space into 18 dimension. In
the 18-dimensional feature space, it calculates an F0-dependent mean function and an F0-normalized covariance,
and finally applies the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musicalinstruments shows that the proposed method improved the recognition rate from 75.73% to 79.73%.
Keywords: musical instrument identification, the pitch dependency, fundamental frequency, automatic musictranscription, computational auditory scene analysis
1.

Introduction

Computational auditory scene analysis as well as visual scene analysis is important to augment communication channels between humans and computers and to∗ This research was partially supported by the Ministry of Education,

Culture, Sports, Science and Technology (MEXT), Grant-in-Aid for
Scientific Research (A), No.15200015, and Informatics ResearchCenter for Development of Knowledge Society Infrastructure (COEprogram of MEXT, Japan).

achieve sophisticated human-computer interaction [8].
However, auditory information has not been used extensively except for speech. In particular, musical instrument identification is an important subtask for thecomputational auditory scene analysis.
Musical instrument identification is also importantfrom a point of view of application. For example, auser wants to search for certain types of musical pieces,such as piano solos or string quartets, a retrieval system can use the results of musical instrument identification. Furthermore, the results of musical instrument

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Kitahara, Goto and Okuno

identification will be important cues for reducing ambiguity when automatically transcribing a musical piece
played on multiple instruments.
The difficulties in musical instrument identification
reside in the fact that most of features of musical sounds
depend on some factors including pitch. In particular,
timbres of musical instruments are obviously affectedby the pitch due to their wide range of pitch.1 For example, the pitch range of the piano covers over sevenoctaves.
To attain high performance of musical instrument
identification, it is indispensable to cope with this pitch
dependency of timbre. Most studies on musical instrument identification, however, have not dealt with thepitch dependency [1–5]. Martin used 31 features including spectral and temporal features with hierarchical classification and attained about 70% of identification by the benchmark of 1,023 solo tones playedby 14 instruments. He pointed out the importance ofthe pitch dependency, but left it as future work [5].
Eronen et al. used spectral and temporal features aswell as cepstral coefficients used by Brown [1] and attained about 80% of identification by the benchmarkof 1,498 solo tones played by 30 instruments [2]. They
treated the pitch as one element of feature vectors, but
did not cope with the pitch dependency. Kashino et al.
also treated the pitch similarly in their automatic music transcription system [4]. They also coped with thedifference of individuals of musical instruments (forexample, the difference of sounds of Yamaha’s pianos
and Boesendolfer’s ones) by template adaptation, but
did not deal with the pitch dependency [6].
In this paper, to take into consideration the pitch
dependency of timbre, we propose a method for modeling the pitch dependency of timbre, F0-dependent
multivariate normal distribution. Each feature or basic
vector of features extracted from musical instrument
sounds is represented by the F0-dependent multivariate
normal distribution, the mean of which is represented
by a function of fundamental frequency (F0). ThisF0-dependent mean function represents the pitch dependency of each feature, while the F0-normalized
covariance represents the non-pitch dependency.
Musical instrument identification is performed both atindividual-instrument level and at non-tree category
level by a discriminant function based on the Bayes
decision rule.
The rest of this paper is organized as follows:
Section 2 proposes the F0-dependent multivariate normal distribution, and Section 3 describes a discriminantfunction based on the Bayes decision rule. Sections 4
and 5 report the experimental results. Finally, Section 6
concludes this paper.
2.

Musical Instrument Identification
using F0-dependent Multivariate
Normal Distribution

The key idea of our method is to approximate the pitch
dependency of each feature representing timbres of musical instrument sounds as a function of fundamental
frequency (F0). An F0-dependent multivariate normal
distribution has two parameters: an F0-dependent meanfunction and an F0-normalized covariance. The former
represents the pitch dependency of features and the latter represents the non-pitch dependency. The reason
why the mean of a distribution of tone features is approximated as a function of F0 is that tone featuresat different pitches have different positions (means) ofdistributions in the feature space. Approximating themean of the distributions as a function of F0 makes it
possible to model how the features vary according to
the pitch with a small set of parameters.
2.1.

Frequency Analysis

Given a musical instrument signal, it is first analyzed by
the short-time Fourier transform (STFT) with a 4096point Hanning window for every 10 ms, and spectral
peaks are extracted from the power spectrum. Then theharmonic structure and the F0 is obtained from thesepeaks.
2.2.

Feature Extraction

The following 129 features are extracted:(1) Spectral Features
1 Spectral centroid
2 Relative power of the fundamental component
3 – 30 Relative cumulative power of from fundamental to i-th components (i = 2, 3, . . . , 29)
31 Relative power in odd and even components
32 – 40 The number of the components the duration of which is p% longer than the longest duration ( p = 10, 20, . . . , 90)

Pitch-Dependent Identification of Musical Instrument Sounds(2) Temporal Features
41 Gradient of the straight line approximating thepower envelope
42 – 58 Average differential of the power envelope during t-sec interval from the onset time(t = 0.15, 0.20, . . . , 0.95)
59 – 75 The ratio of the power at t-sec after the
onset time(3) Modulation Features
76 The amplitude of AM
77 The frequency of AM
78 The amplitude of FM
79 The frequency of FM
80 The amplitude of the spectral centroid modulation
81 The frequency of the spectral centroid modulation
82 – 94 The amplitude of k-th MFCC

Whereas the spectral, temporal and modulation features are designed based on previous studies, the peakkurtosis features are original and have not been usedin previous studies. The kurtosis of spectral peaks isrelated to how large non-harmonic components are included. If a sound has small non-harmonic components,
spectral peaks will have large kurtosis. If it has large
non-harmonic components, they will have small kurtosis (see Fig. 1). These features are therefore used formodeling the degree of incorporation of non-harmoniccomponents, which has not been considered in previous
studies.
After the feature extraction, the feature space is standardized and then the dimensionality of it is reduced by
two methods: the 129-dimensional feature space is reduced to a 79-dimensional one by principal component
analysis (PCA) with the proportion value of 99%, andthen is further reduced to a lower-dimensional one bylinear discriminant analysis (LDA). In this paper, thefeature space is reduced to an 18-dimensional space,since we deal with 19 instruments.

2.3.

95 – 107 The frequency of k-th MFCC
(4) Peak Kurtosis Features
108 – 118 Temporal average of the kurtosis of spectral peaks in the i-th component(i = 1, 2, . . . , 11)
119 – 129 The amplitude of the temporal modulation of the kurtosis of spectral peaks in the i-thcomponent (i = 1, 2, . . . , 11)Parameter Estimation of F0-dependent
Multivariate Normal Distribution

For each instrument ωi , the parameters of the
F0-dependent multivariate normal distribution, the F0dependent mean function µi ( f ) and the F0-normalized
covariance i , are calculated. The F0-dependent meanfunction is obtained through approximating each element of the feature vectors as a cubic polynomial withthe least-square method. For example, piano’s fourth
basic vector and cello’s first basic vector, obtained with0Power [dB]0Power [dB]
–100
0

269

2000
Frequency [Hz]4000(a) Recorder (C5, Forte)

–100
0

2000
Frequency [Hz]4000(b) Alto Sax (C5, Forte)

Figure 1. Power spectra of two different instruments at the onset time. These show that spectral peaks of an instrument with large non-harmoniccomponents, such as (b) Alto Sax, has small kurtosis in contrast to those of an instrument with small non-harmonic components, such as (a)Recorder.

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Kitahara, Goto and Okuno

2.4.

Feature3

Once pitch and non-pitch dependencies of feature vectors are represented by the F0-dependent multivariate
normal distribution, the Bayes decision rule is applied
to identify the name or category of musical instruments.
The discriminant function gi (x; f ) for the musical instrument ωi is defined by

0

–3
27.5

110
440
1760
Fundamental Frequency [Hz]gi (x; f ) = log p(x | ωi ; f ) + log p(ωi ; f ),(1)

where x is the feature vector of an input sound,p(x | ωi ; f ) is the probability density function (PDF)of this distribution and p(ωi ; f ) is a priori probabilityof the instrument ωi .
The PDF of this distribution is defined by


1
1 2
p(x|ωi ; f ) =
D
exp
−
(x,
µ
(
f
))
,
i
(2π )d/2 |i |1/2
2
(2)

Feature

2

0
55

110
220
440
880
Fundamental Frequency [Hz]Figure 2. Examples of F0-dependent mean functions. Because thevertical axes of these graphs are not features defined in Section 2.1
but those obtained after standardization and dimensionality reduction (PCA and LDA), they do not have any units. In these graphs,
we plot all the data of pianos or cellos in our database, while F0dependent mean functions are estimated by only from training datain our experiments.

the two successive dimensionality reduction method(PCA and LDA), are depicted in Fig. 2(a) and (b), respectively. Then, the F0-normalized covariance is calculated by the following equation:
i =Application of the Bayes Decision Rule1 (x − µi ( f x ))(x − µi ( f x )) ,n i x ∈χiwhere  is the transposition operator, χi and n i are theset of the training data of the instrument ωi and its total
number, respectively. f x denotes the F0 of the feature vector x. Because the F0-dependent mean function represents the pitch dependency of features, theF0-normalized covariance, obtained by subtracting themean from each feature, eliminates the pitch dependency of features.

where d is the number of dimensions of the feature
space and D 2 is the squared Mahalanobis distance defined by
D 2 (x, µi ( f )) = (x − µi ( f )) i−1 (x − µi ( f )).
Substituting Eq. (2) into Eq. (1), thus, generates thediscriminant function gi (x; f ) as follows:11dgi (x; f ) = − D 2 (x, µi ( f )) − log |i | − log 2π222+ log p(ωi ; f ).
The name of the instrument that maximizes this function, that is ωk satisfying k = argmaxi gi (x; f ), is determined as the result of musical instrument identification.
The a priori probability p(ωi ; f ) represents whether
the pitch range of the instrument ωi includes f ,that is,
1/c (if f ∈ Ri )p(ωi ; f ) =0(if f ∈ Ri )where Ri is the pitch range of the instrument
ωi , andc is the normalizing factor to satisfy i p(ωi ; f ) =1.

Pitch-Dependent Identification of Musical Instrument Sounds

3.

Experiments and Results

3.1.

Experimental Conditions

We conducted experiments on musical instrument identification for investigating improvement of the performance by the proposed method. We obtained the recognition rates by the commonly used multivariate normal
distribution (called baseline) and by the proposed F0dependent multivariate normal distribution, and compared them.
The benchmark used for evaluation is a subset of the“RWC Music Database: Musical Instrument Sound”(RWC-MDB-I-2001) [7], which is a large musical instrument sound databalse available to researchers aroundthe world. This subset summarized in Table 1 was selected by the quality of recorded sounds and consists of
6,247 solo tones of 19 orchestral instruments. All data
are sampled at 44.1 kHz with 16 bits. We first divided
the whole data into 10 groups, and then repeated thefollowing step 10 times: each time, we left out one of
the 10 groups for training and used the omitted one fortesting. That means that nine tenths of the data listed inTable 1 were used for calculating F0-dependent mean

Table 1.

functions and F0-normalized covariances. This experiment technique is called 10-fold cross validation.
We evaluated the category-level performance of our
method, because the category of instruments is usefulfor some applications including music retrieval. For
example, when a user wants to find a piece of pianosolo on a music retrieval system, the system can rejectpieces containing instruments of different categories,
which can be judged without identifying individual instrument names. We adopted the categories of musicalinstruments summarized in Table 2, which are determined based on the sounding mechanisms of instruments and existing studies [2, 5].

3.2.

Results of Musical Instrument Identification

Table 3 summarizes recognition rates by both baseline
and proposed methods. The proposed F0-dependent
method improved the recognition rates at individualinstrument level from 75.73 to 79.73% and at category
level from 88.20 to 90.65% in average. It also reduced
recognition errors by 16.48 and 20.67% in average at
individual-instrument and category levels, respectively.

Contents of the database used in this paper.

Instrument name (Abbrev.)

pitch range# of tones# of individuals

Intensity

Articulation3

Forte, normal

Normal only

Piano (PF)

A0–C8

508

Classical Guitar (CG)

E2–E5

696

Ukulele (UK)

F3–A5

295

Acoustic Guitar (AG)

E2–E5

666
528

271

& pianoViolin (VN)

G3–E7

Viola (VL)

C3–F6

472

Cello (VC)

C2–F5

558

Trumpet (TR)E3–A6151

2
3

Trombone (TB)A1–F5

262

Soprano Sax (SS)G3–E6

169

Alto Sax (AS)C3–A5282

Tenor Sax (TS)G2–E5153

Baritone Sax (BS)

C2–A4

215

Oboe (OB)A3–G6151

2

Faggoto (FG)A1–D5

312

3

Clarinet (CL)

D3–F6

263

Piccolo (PC)

D5–C8

245

Flute (FL)

C4–C7

134

2

Recorder (RC)

C4–B6

160

3

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Kitahara, Goto and Okuno

Table 2.

Categorization of 19 instruments.

Category

Instruments (abbreviation)

Piano

Piano (PF)Guitars

Classical Guitar (CG), Ukulele (UK),
Acoustic Guitar (AG)Strings

Violin (VN), Viola (VL), Cello (VC)

Brasses

Trumpet (TR), Trombone (TB)

Saxophones

Soprano Sax (SS), Alto Sax (AS),
Tenor Sax (TS), Baritone Sax (BS)Double Reeds

Oboe (OB), Faggoto (FG)Clarinet

Clarinet (CL)

Air Reeds

Piccolo (PC), Flute (FL), Recorder (RC)The observation of these experimental results is summarized below:• The recognition rates of six instruments (Piano (PF),Trumpet (TR), Trombone (TB), Soprano Sax (SS),
Table 3.

Baritone Sax (BS), and Faggoto (FG)) were improved by more than 7%. In particular, the recognition rate for pianos was improved by 9.06%, andits recognition errors were reduced by 35.13%. This
big improvement was attained since their pitch dependency is salient due to their wide range of pitch.
• The recognition rates for the four types of saxophones at individual-instrument level (47–73%)
were lower than those at category level (77–92%).
This is because sounds of these saxophones werequite similar. In fact, Martin reported that sounds ofvarious saxophones are very difficult even for humans (music experts) to discriminate [5].
• Since we adopt the flat (non-hierarchical) categorization, the recognition rates at category level depend on the category. The recognition rates of GUITARS and STRINGS at category level were more than
94%, while those of BRASSES, SAXOPHONES, DOUBLE REEDS, CLARINET and AIR REEDS were about
70–90%. This is because instruments of these categories have similar sounding mechanism: these

Accuracy by usual distribution (baseline) and F0-dependent distribution (proposed).
Individual-instrument level

Category level

Baseline

Proposed

Improv.

Baseline

Proposed

Improv.

PF

74.21%

83.27%+9.06%

74.21%

83.27%+9.06%

CG

90.23%

90.23%±0.00%

97.27%

97.13%−0.14%

UK

97.97%

97.97%±0.00%

97.97%

98.31%+0.34%

AG

81.23%

83.93%+2.70%

94.89%

95.65%+0.76%

VN

69.70%

73.67%+3.97%

98.86%

99.05%+0.19%

VL

73.94%

76.27%+2.33%

93.22%

94.92%+1.70%

VC

73.48%

78.67%+5.19%

95.16%

96.24%+1.08%

TR

73.51%

82.12%+8.61%

76.82%

85.43%+8.61%

TB

76.72%

84.35%+7.63%

85.50%

89.69%+4.19%

SS

56.80%

65.89%+9.09%

73.96%

80.47%+6.51%

AS

41.49%

47.87%+6.38%

73.76%

77.66%+3.90%

TS

64.71%

66.01%+1.30%

90.20%

92.16%+1.96%

BS

66.05%

73.95%+7.90%

81.40%

86.05%+4.65%

OB

71.52%

72.19%+0.67%

75.50%

74.83%−0.67%

FG

59.61%

68.59%+8.98%

64.74%

71.15%+6.41%

CL

90.69%

92.07%+1.38%

90.69%

92.07%+1.38%

PC

77.56%

81.63%+4.07%

89.39%

90.20%+0.81%

FL

81.34%

85.07%+3.73%

82.09%

85.82%+3.73%

RC

91.88%

91.25%−0.63%

92.50%

91.25%−1.25%

Ave.

75.73%

79.73%+4.00%

88.20%

90.65%+2.45%

Baseline: Usual (F0-independent) distribution.
Proposed: F0-dependent distribution.

Pitch-Dependent Identification of Musical Instrument Sounds

categories are subcategories of “wind instruments”
in conventional hierarchical categorization.
• The readers might think that a typical alternative approach of dealing with the pitch dependency could
be a method that simply adds the value of the F0 to
the feature vector (i.e., the number of dimensions of
the feature vector is increased by one). We therefore
compared this alternative method with our method.
The experimental results showed that the alternative
method is inferior to our method: the recognition rate
was 75.88% on average, which is almost same withthe baseline method.

4.

Evaluation of the Bayes Decision Rule

The effect of the Bayes decision rule in musical instrument identification was evaluated by comparing withthe k-NN rule (k-nearest neighbor rule; k = 3 in thispaper) with/without LDA. Three variations of the dimensionality reduction are examined:
Table 4.

(a) Reduction to 79 dimension by PCA,(b) reduction to 18 dimension by PCA, and(c) reduction to 18 dimension by PCA and LDA.
The last one is adopted in the proposed method.
The experimental results listed in Table 4 showedthat the proposed Bayes decision rule performed betterin average than the 3-NN rule. Some observations are
as follows:• The Bayes decision rule with 79-dimension showedpoor performance for Acoustic Guitar (AG), Trumpet(TR), Soprano Sax (SS), Tenor Sax (TS), Oboe (OB),and Flute (FL), since there are insufficient training
data to estimate parameters of a 79-dimensional normal distribution. For small training sets with 79dimension, k-NN is superior to the Bayes decision
rule.
• LDA with the Bayes decision rule improved the accuracy of musical instrument identification from 66.50
to 79.73% on average. Although it seemed that PCA

Accuracy by k-NN rule and the Bayes decision rule.
k-NN rule (k = 3)
79-Dim.

Bayes decision rule18-Dim.
PCA

273

79-Dim.

PCA&LDA

18-Dim.
PCA

PCA&LDAPF

53.94%

46.46%

63.39%

55.91%

59.06%

83.27%

CG

79.74%

77.16%

75.72%

98.28%

97.27%

90.23%

UK

94.58%

92.54%

97.63%

67.12%

80.00%

97.97%

AG

95.05%

92.79%

97.00%

19.97%

44.14%

83.93%

VN

47.73%

46.02%

45.83%

89.58%

84.47%

73.67%

VL

55.93%

54.24%

61.86%

71.19%

79.24%

76.27%

VC

86.20%

85.84%

84.23%

45.16%

30.82%

78.67%

TR

36.42%

38.41%

47.02%

41.72%

72.85%

82.12%

TB

70.99%

54.58%

77.86%

75.19%

78.24%

84.35%

SS

23.08%

14.20%

24.85%

48.52%

66.86%

65.89%

AS

37.59%

29.79%

40.43%

72.70%

41.84%

47.84%

TS

62.09%

66.01%

68.63%

30.07%

61.44%

66.01%

BS

68.84%

67.91%

66.98%

55.35%

54.42%

73.95%

OB

47.68%

48.34%

49.01%

43.71%

81.46%

72.19%

FG

64.10%

65.06%

74.36%

40.38%

30.12%

68.59%

CL

93.45%

87.93%

93.10%

95.51%

93.45%

92.07%

PC

84.08%

84.90%

84.08%

63.27%

58.37%

81.63%

FL

88.06%

72.39%

94.03%

35.82%

84.33%

85.07%

RC

97.50%

93.75%

97.50%

85.00%

96.25%

91.25%

Ave.

70.27%

66.98%

72.53%

62.11%

66.50%

79.73%

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Kitahara, Goto and Okuno

with 79-dimension performed better than LDA forClassical Guitar (CG), Violin (VN), and Alto Sax(AS), the cumulative performance of LDA for the
categories of strings and saxophones is better thanthat of PCA.
• We did not conduct the experiment using only LDA.
This is because LDA cannot be applied to featuresthat are highly correlative: the inverse matrix of thewith-in covariance, which is used by LDA, is not accurately calculated when the feature space includes
some highly correlative dimensions. Because PCA
not only reduces the dimensionality but also orthogonalizes the feature space, for our features, some ofwhich are highly correlative, using PCA before LDA
is effective.

5.

Conclusions

In this paper, we presented a method for musical instrument identification using the F0-dependent multivariate normal distribution which takes into consideration the pitch dependency of timbre. The method improved the recognition rates at individual-instrument
level from 75.73 to 79.73%, and at category levelfrom 88.20 to 90.65% on average, respectively. The
Bayes decision rule with dimensionality reduction by
PCA and LDA also performed better than the 3-NN
method.
Future work will include to extend our method todeal with real-world music signals such as musicalpieces and phrases containing several sounds of thesame instrument. From the viewpoint of computational auditory scene analysis, it is also important todeal with a variety of sounds including non-musicalsounds. We therefore plan to extend our method todeal with such general sounds because we can expect that our approach of handling the pitch dependency is also effective for general sounds that havepitches.

Acknowledgments
We thank everyone who has contributed to building
and distributing the RWC Music Database (Musical Instrument Sound: RWC-MDB-I-2001) [7]. We also thankDr. Kazuhiro Nakadai and Dr. Hideki Asoh for theirvaluable comments.

Note
1. In this paper, pitch and timbre are used as the meanings of fundamental frequency and acoustic features for discriminating instruments, respectively.

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Tetsuro Kitahara received the B.S. from Tokyo University of Science in 2002 and the M.S. from Kyoto University in 2004. He iscurrently a Ph.D. course student at Graduate School of Informatics, Kyoto University. Since 2005, he has been a Research Fellowof the Japan Society for the Promotion of Science. His research interests include music informatics. He recieved IPSJ 65th National
Convention Student Award in 2003, IPSJ 66th National Convention Student Award and TELECOM System Technology Award forPitch-Dependent Identification of Musical Instrument Sounds

Student in 2004, and IPSJ 67th National Convention Best Paper
Award for Young Researcher in 2005. He is a student member of
IPSJ, IEICE, JSAI, ASJ, and JSMPC.

Masataka Goto received his Doctor of Engineering degree in Electronics, Information and Communication Engineering from Waseda
University, Japan, in 1998. He then joined the Electrotechnical Laboratory (ETL; reorganized as the National Institute of Advanced
Industrial Science and Technology (AIST) in 2001), where he has
been engaged as a researcher ever since. He served concurrently as a
researcher in Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Corporation (JST)from 2000 to 2003, and an associate professor of the Department
of Intelligent Interaction Technologies, Graduate School of Systems
and Information Engineering, University of Tsukuba since 2005. His
research interests include music information processing and spoken
language processing. Dr. Goto received seventeen awards including the IPSJ Best Paper Award and IPSJ Yamashita SIG ResearchAwards (MUS and SLP) from the Information Processing Society ofJapan (IPSJ), Awaya Prize for Outstanding Presentation and Award
for Outstanding Poster Presentation from the Acoustical Society ofJapan (ASJ), Award for Best Presentation from the Japanese Society
for Music Perception and Cognition (JSMPC), WISS 2000 Best Pa-

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per Award and Best Presentation Award, and Interaction 2003 Best
Paper Award. He is a member of the IPSJ, ASJ, JSMPC, Instituteof Electronics, Information and Communication Engineers (IEICE),
and International Speech Communication Association (ISCA).

Hiroshi G. Okuno received the B.A. and Ph.D from the Universityof Tokyo in 1972 and 1996, respectively. He worked for Nippon Telegraph and Telephone, Kitano Symbiotic Systems Project, and Tokyo
University of Science. He is currently a professor at the Department
of Intelligence Technology and Science, Graduate School of Informatics, Kyoto University. He was a visiting scholar at Stanford University, and a visiting associate professor at the University of Tokyo.
He has done research in programming languages, parallel processing, and reasoning mechanism in AI, and he is currently engaged
in computational auditory scene analysis, music scene analysis androbot audition. He received the best paper awards from the JapaneseSociety for Artificial Intelligence and the International Society for
Applied Intelligence, in 1991 and 2001, respectively. He edited withDavid Rosenthal “Computational Auditory Scene Analysis” from
Lawrence Erlbaum Associates in 1998 and with Taiichi Yuasa “Advanced Lisp Technology” from Taylor and Francis Inc. in 2002. He
is a member of IPSJ, JSAI, JSSST, JSCS, ACM, AAAI, ASA, andIEEE.